Our key insight is that the `where to look?' question for ObjectNav can be treated as a pure perception problem, and learned without any interactions in a simulated 3D environment. Specifically, we train the potential function network \(\pi _{pf}\)  on a dataset of semantic maps that are pre-computed from semantic annotations in 3D scene datasets [1]}, [2]}.The same semantic annotations are used for most ObjectNav methods. First, we project the semantic point-cloud annotations for a 3D scene to per-floor 2D semantic maps using the publicly available code from Semantic MapNet [3]}. We then apply random translations and rotations to the map as a form of data augmentation. This gives an augmented and complete semantic map \(m^c\)  of the 3D scene with two channels for obstacles and explored regions, and \(N\)  channels for objects (see column 1 in Fig. REF ).
